Premup_analysis
This set of scripts are intended for the priors and flat versions of the PREMUP paradigm. We need to select below here which version to look at.
Compute individual performance indeces
# Initialize
strong_acc <- matrix(data=NA, nrow=40+100, ncol=max(exp.p$which_sub))
weak_acc <- matrix(data=NA, nrow=40+35, ncol=max(exp.p$which_sub))
flat_acc <- matrix(data=NA, nrow=40+30, ncol=max(exp.p$which_sub))
# temp <- NA
# Initialize plot
# par(mfrow=c(exp.p$nSubs/2,4), oma=c(0,0,0,0)) # Subplots
# Loop over subjects
for(cSub in exp.p$which_sub){
# Get subject code
sub_code <- paste("sub-", sprintf("%03d", cSub), sep="")
# Get one subject's data
sub_data <- subset(x = learning, subset = participant == cSub) # Data
load(paste(cleaned_dir, "../../data/BIDS/", sub_code,"/", sub_code, "_params.RData", sep="")) # Parameters
# Get cummulative accuracy split by scenes
for (cScn in seq(1,6, by=2)){
cumm_acc <- cumsum(sub_data$trial_acc_manual[sub_data$scn_cat==cScn])
cumm_acc2 <- cumsum(sub_data$trial_acc_manual[sub_data$scn_cat==cScn+1])
cumm_acc <- cumm_acc / c(1:length(cumm_acc)) # Cummulative accuracy
cumm_acc2 <- cumm_acc2 / c(1:length(cumm_acc2)) # Cummulative accuracy
# Get current scene's info
curr_scn_cond <- unique(sub_data$scn_condition[sub_data$scn_cat==cScn])
curr_scn_cond_label <- p$scn_cont[curr_scn_cond]
# Store average perfromance
if(curr_scn_cond_label==0.9){
strong_acc[1:length(cumm_acc),cSub] <- rowMeans(cbind(cumm_acc, cumm_acc2))
}else if(curr_scn_cond_label==0.7) {
weak_acc[1:length(cumm_acc),cSub] <- rowMeans(cbind(cumm_acc, cumm_acc2))
}else{
flat_acc[1:length(cumm_acc),cSub] <- rowMeans(cbind(cumm_acc, cumm_acc2))
}
# Plot
plot(cumm_acc, ylim = c(0,1), xlim = c(0,length(cumm_acc)),xaxt="n", col="pink",
main = paste(curr_scn_cond_label, "contingency"))
text(labels=sub_code, x=0, y=1.2, xpd=T)
abline(h=.5, col = "red", lty = 2); abline(h=curr_scn_cond_label ,col = "green")
abline(v=0, col = "black", lty = 4);abline(v=40, col = "black", lty = 4);
xtick<-c(0,40)
axis(side=1, at=xtick, labels = FALSE)
text(x=xtick, par("usr")[3],
labels = c("Refresh", "Enc starts"), pos = 1, xpd = TRUE)
points(seq(1, length(cumm_acc2)), cumm_acc2, col = "blue")
}
}
Plot both tasks
par(mfrow=c(1,3)) # Subplots
prior_label=c("Strong prior", "Weak prior", "Flat prior")
p$scn_cont <-c(.9, .7, .5)
for(cCond in c(1:3)){
if(cCond==1){
### Strong prior
data_mean=rowMeans(strong_acc, na.rm = T)
data_sd=rowSds(strong_acc, na.rm = T)
ind=c(1:140, 140:1)
}else if(cCond==2){
data_mean=rowMeans(weak_acc, na.rm = T)
data_sd=rowSds(weak_acc, na.rm = T)
ind=c(1:75, 75:1)
}else{
data_mean=rowMeans(flat_acc, na.rm = T)
data_sd=rowSds(flat_acc, na.rm = T)
ind=c(1:70, 70:1)
}
# SD
xx <- data_mean+data_sd
yy <- data_mean-data_sd
# Plot
plot(data_mean, ylim = c(0,1), xlim = c(0,length(data_mean)),xaxt="n", col="blue",
main = prior_label[cCond])
polygon(x= ind, y = c((xx),rev(yy)), col = rgb(.5,.5,.8,.3), border = "red")
# Format
abline(h=max(p$scn_cont[cCond]) ,col = "green")
abline(h=.5, col = "red", lty = 2);
abline(v=0, col = "black", lty = 4);abline(v=40, col = "black", lty = 4);
xtick<-c(0,40)
axis(side=1, at=xtick, labels = FALSE)
text(x=xtick, par("usr")[3],
labels = c("Refresh", "Encoding starts"), pos = 1, xpd = TRUE)
}
Compute performance only during encoding
# Get encoding data
learning <- encoding
# Initialize
strong_acc <- matrix(data=NA, nrow=100, ncol=max(exp.p$which_sub))
weak_acc <- matrix(data=NA, nrow=35, ncol=max(exp.p$which_sub))
flat_acc <- matrix(data=NA, nrow=30, ncol=max(exp.p$which_sub))
# temp <- NA
# Initialize plot
# par(mfrow=c(exp.p$nSubs/2,4), oma=c(0,0,0,0)) # Subplots
# Loop over subjects
for(cSub in exp.p$which_sub){
# Get subject code
sub_code <- paste("sub-", sprintf("%03d", cSub), sep="")
# Get one subject's data
sub_data <- subset(x = learning, subset = participant == cSub) # Data
load(paste(cleaned_dir, "../../data/BIDS/", sub_code,"/", sub_code, "_params.RData", sep="")) # Parameters
# Get cummulative accuracy split by scenes
for (cScn in seq(1,6, by=2)){
cumm_acc <- cumsum(sub_data$trial_acc_manual[sub_data$scn_cat==cScn])
cumm_acc2 <- cumsum(sub_data$trial_acc_manual[sub_data$scn_cat==cScn+1])
cumm_acc <- cumm_acc / c(1:length(cumm_acc)) # Cummulative accuracy
cumm_acc2 <- cumm_acc2 / c(1:length(cumm_acc2)) # Cummulative accuracy
# Get current scene's info
curr_scn_cond <- unique(sub_data$scn_condition[sub_data$scn_cat==cScn])
curr_scn_cond_label <- p$scn_cont[curr_scn_cond]
# Store average perfromance
if(curr_scn_cond_label==0.9){
strong_acc[1:length(cumm_acc),cSub] <- rowMeans(cbind(cumm_acc, cumm_acc2))
}else if(curr_scn_cond_label==0.7) {
weak_acc[1:length(cumm_acc),cSub] <- rowMeans(cbind(cumm_acc, cumm_acc2))
}else{
flat_acc[1:length(cumm_acc),cSub] <- rowMeans(cbind(cumm_acc, cumm_acc2))
}
}
}Plot encoding
par(mfrow=c(1,3)) # Subplots
prior_label=c("Strong prior", "Weak prior", "Flat prior")
p$scn_cont <-c(.9, .7, .5)
for(cCond in c(1:3)){
if(cCond==1){
### Strong prior
data_mean=rowMeans(strong_acc, na.rm = T)
data_sd=rowSds(strong_acc, na.rm = T)
ind=c(1:100, 100:1)
}else if(cCond==2){
data_mean=rowMeans(weak_acc, na.rm = T)
data_sd=rowSds(weak_acc, na.rm = T)
ind=c(1:35, 35:1)
}else{
data_mean=rowMeans(flat_acc, na.rm = T)
data_sd=rowSds(flat_acc, na.rm = T)
ind=c(1:30, 30:1)
}
# SD
xx <- data_mean+data_sd
yy <- data_mean-data_sd
# Plot
plot(data_mean, ylim = c(0,1), xlim = c(0,length(data_mean)),xaxt="n", col="blue",
main = prior_label[cCond])
polygon(x= ind, y = c((xx),rev(yy)), col = rgb(.5,.5,.8,.3), border = "red")
# Format
abline(h=max(p$scn_cont[cCond]) ,col = "green")
abline(h=.5, col = "red", lty = 2)
#abline(v=0, col = "black", lty = 4);abline(v=40, col = "black", lty = 4);
xtick<-c(0,40)
axis(side=1, at=xtick, labels = FALSE)
#text(x=xtick, par("usr")[3]
#labels = c("Refresh", "Encoding starts"), pos = 1, xpd = TRUE)
}
Save cleaned data
#write.csv(learning_data, file = paste(output_dir, sub_code, "_task-learning_cleaned.csv", sep=""))
#write.csv(rating_data, file = paste(output_dir, sub_code, "_task-rating_cleaned.csv", sep=""))